research-article

CRACS: Compaction of Rules in Anticipatory Classifier Systems

Authors:

Pierre Parrend,

Anne Jeannin-GirardonAuthors Info & Claims

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Pages 1838 - 1845

https://doi.org/10.1145/3583133.3596352

Published: 24 July 2023 Publication History

Abstract

Rule Compaction of populations of Learning Classifier Systems (LCS) has always been a topic of interest to get more insights into the discovered underlying patterns from the data or to remove useless classifiers from the populations. However, these techniques have neither been used nor adapted to Anticipatory Learning Classifier Systems (ALCS). ALCS differ from other LCS in that they build models of their environments from which decision policies to solve their learning tasks are learned. We thus propose CRACS (Compaction of Rules in Anticipatory Classifier Systems), a compaction algorithm for ALCS that aims to reduce the size of their environmental models without impairing these models or the ability of these systems to solve their tasks. CRACS relies on filters applied to classifiers and subsumption principles. The capabilities of our compaction algorithm have been studied with three different ALCS on a thorough benchmark of 23 mazes of various levels of environmental uncertainty. The results show that CRACS reduces the size of populations of classifiers while the learned models of environments and the ability of ALCS to solve their tasks are preserved.

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Cited By

Siddique AHeider MIqbal MShiraishi HLi XHandl J(2024)A Survey on Learning Classifier Systems from 2022 to 2024Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664165(1797-1806)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664165

Index Terms

CRACS: Compaction of Rules in Anticipatory Classifier Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Rule learning

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cover image ACM Conferences

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation

July 2023

2519 pages

ISBN:9798400701207

DOI:10.1145/3583133

Chair:
Sara Silva,
Program Chair:
Luís Paquete

Copyright © 2023 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 24 July 2023

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Université de Strasbourg

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GECCO '23 Companion

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GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation

July 15 - 19, 2023

Lisbon, Portugal

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Cited By

Siddique AHeider MIqbal MShiraishi HLi XHandl J(2024)A Survey on Learning Classifier Systems from 2022 to 2024Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664165(1797-1806)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664165

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